Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.
Mila（米拉研究所）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；Stanford University（斯坦福大学）；HEC Montréal（蒙特利尔高等商学院）
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link prediction and complex query answering tasks by learning representations of entities, relations, and queries. Still, most existing query answering methods rely on transductive entity embeddings and cannot generalize to KGs containing new entities without retraining the entity embeddings. In this work, we study the inductive query answering task where inference is performed on a …
PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion
School of Mathematics, Shandong University（山东大学数学学院）；School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学物理与数学科学学院）；Data Science Institute, Shandong University（山东大学数据科学研究院）
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
The Chinese University of Hong Kong（香港中文大学）；International Digital Economy Academy（国际数字经济研究院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Cambridge（剑桥大学）
Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-Starting Progressive Propagation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学健康医疗大数据国家研究院）
Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion
Nanjing University of Science and Technology（南京理工大学）；Jinan University（济南大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Shanxi University（山西大学）；Nanyang Technological University（南洋理工大学）；Beijing University Of Technology（北京理工大学）
A Foundation Model for Zero-shot Logical Query Reasoning
Intel AI Lab（英特尔人工智能实验室）；Purdue University（普渡大学）；Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Chair（加拿大高级研究院人工智能讲席）；Université de Montréal（蒙特利尔大学）
Over the years, reasoning over knowledge graphs (KGs), which aims to infer new conclusions from known facts, has mostly focused on static KGs. The unceasing growth of knowledge in real life raises the necessity to enable the inductive reasoning ability on expanding KGs. Existing inductive work assumes that new entities all emerge once in a batch, which oversimplifies the real scenario that new entities continually appear. This study dives into a more realistic and challenging setting where new entities emerge in multiple batches. We propose a walk-based inductive reasoning model to tackle the …
Control flow compilation is a hybrid between classical WAM compilation and meta-call, limited to the compilation of non-recursive clause bodies. This approach is used successfully for the execution of dynamically generated queries in an inductive logic programming setting (ILP). Control flow compilation reduces compilation times up to an order of magnitude, without slowing down execution. A lazy variant of control flow compilation is also presented. By compiling code by need, it removes the overhead of compiling unreached code (a frequent phenomenon in practical ILP settings), and thus reduces…
Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
Neuro-Symbolic Recommendation Model based on Logic Query
Huzhou University（湖州师范学院）；Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources（浙江省现代农业资源智慧管理与应用重点实验室）；Guizhou University（贵州大学）；Guizhou Academy of Sciences（贵州科学院）
LARODEC Laboratory – Institut Supérieur de Gestion de Tunis（突尼斯高等管理学院LARODEC实验室）
Since formulation of Inductive Database (IDB) problem, several Data Mining (DM) languages have been proposed, confirming that KDD process could be supported via inductive queries (IQ) answering. This paper reviews the existing DM languages. We are presenting important primitives of the DM language and classifying our languages according to primitives' satisfaction. In addition, we presented languages' syntaxes and tried to apply each one to a database sample to test a set of KDD operations. This study allows us to highlight languages capabilities and limits, which is very useful for future wor…
Guangzhou University（广州大学）；Zhejiang University（浙江大学）；The University of Hong Kong（香港大学）
Recent studies have revealed that neural combinatorial optimization (NCO) has advantages over conventional algorithms in many combinatorial optimization problems such as routing, but it is less efficient for more complicated optimization tasks such as packing which involves mutually conditioned action spaces. In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems. We first embed states by a recurrent encoder, and then adopt attention with conditional queries from previous actions. The conditional query mechanism fills the informa…
